7 Step 5 - Check ID on Camera

8 Problem Description

This document explains the process of verifying camera IDs between two datasets: the camera trap setup data and the species records data. The goal is to identify any mismatches or inconsistencies in camera IDs between these datasets.

9 Problem Solving

9.1 Common steps

To solve this issue, we follow some of the first basic steps from previous checks, as using our customized read_sheet function that provides the full paths of all .xlsx files available in order to read the species sheet from all files.

Code

source("R/FUNCTIONS.R")

9.2 Data Loading

We load two datasets from the spreadsheet: 1. Camera trap setup data (ct) 2. Species records data (rec)

Code

# Read camera trap setup data
ct <- read_sheet(sheet = "Camera_trap")

# Read species records data
rec <- read_sheet(sheet = "Species_records_camera")

9.3 Verification Process

The verification process consists of two main checks:

9.3.1 Records to Camera Trap Check

This check identifies any camera IDs in the species records that don’t exist in the camera trap data:

Code

record_to_ct <- purrr::map2(ct, rec, function(c, r) {
  base::setdiff(r$Camera_id, c$Camera_id) |>
    tibble::enframe(name = "Error", value = "Camera_id") |>
    dplyr::mutate(note = "Record without camera on sheet")
}) |>
  purrr::discard(~ nrow(.x) == 0) |>
  dplyr::bind_rows(.id = "Dataset")

head(record_to_ct)

# A tibble: 6 × 4
  Dataset        Error Camera_id           note                          
  <chr>          <int> <chr>               <chr>                         
1 Carlos_Scarlat     1 Trap6_Bueiro84N     Record without camera on sheet
2 Carlos_Scarlat     2 Trap6_Bueiro40N     Record without camera on sheet
3 NERF               1 <NA>                Record without camera on sheet
4 Paludosa           1 PF03_CANINANA_E1_C2 Record without camera on sheet
5 Paludosa           2 PF03_CANINANA_E1_C3 Record without camera on sheet
6 Paludosa           3 PF03_CANINANA_E1_C4 Record without camera on sheet

9.3.2 Camera Trap to Records Check

This check identifies any camera IDs in the camera trap data that don’t have corresponding records:

Code

ct_to_record <- purrr::map2(ct, rec, function(c, r) {
  base::setdiff(c$Camera_id, r$Camera_id) |>
    tibble::enframe(name = "Error", value = "Camera_id") |>
    dplyr::mutate(note = "Camera without record on sheet")
}) |>
  purrr::discard(~ nrow(.x) == 0) |>
  dplyr::bind_rows(.id = "Dataset")

head(ct_to_record)

# A tibble: 6 × 4
  Dataset                   Error Camera_id  note                          
  <chr>                     <int> <chr>      <chr>                         
1 Ana_Delciellos                1 PNSB_cam1a Camera without record on sheet
2 Ana_Delciellos                2 PNSB_cam2c Camera without record on sheet
3 Ana_Delciellos                3 PNSB_cam4b Camera without record on sheet
4 Ana_Delciellos                4 PNSB_cam5a Camera without record on sheet
5 Ana_Delciellos                5 PNSB_cam6a Camera without record on sheet
6 Arteris_Litoral_Sul_IBAMA     1 CAM-0008   Camera without record on sheet

9.4 Output Generation

Finally, we combine both checks and save the results to an Excel file:

Code

record_to_ct |>
  dplyr::bind_rows(ct_to_record) |>
  openxlsx::write.xlsx("Output/check_id_errors.xlsx", asTable = TRUE)

9.5 Results Interpretation

The output file check_id_errors.xlsx contains two types of errors:

Camera IDs that appear in species records but not in the camera trap data
Camera IDs that appear in the camera trap data but have no corresponding species records

This helps identify potential data entry errors or missing information in either dataset.